Good, better, and most probable recommendations

  • Machine Learning seems to offer the solution to the central problem in recommender systems: Learning to recommend interesting items from observations. However, one tends to run into similar problems each time one tries to apply out-of-the-box solutions from Machine Learning. This article relates the problem of recommendation by user modeling closely to the machine learning problem and explicates some inherent dilemmas. A few examples will illustrate specific approaches and discuss underlying assumptions on the domain or how learned hypotheses relate to requirements on the user model. The article concludes with a tentative 'checklist' that one might like to consider when thinking about to use Machine Learning in User Adaptive environments such as recommender systems.

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Metadaten
Author:Martin E. MüllerORCiDGND
URN:urn:nbn:de:bvb:384-opus4-304
Frontdoor URLhttps://opus.bibliothek.uni-augsburg.de/opus4/43
Series (Serial Number):Reports / Technische Berichte der Fakultät für Angewandte Informatik der Universität Augsburg (2004-17)
Type:Report
Language:English
Year of first Publication:2004
Publishing Institution:Universität Augsburg
Release Date:2006/02/02
Tag:machine learning for user modeling; recommender systems; adaptive user interfaces
GND-Keyword:Maschinelles Lernen; Benutzeroberfläche
Institutes:Fakultät für Angewandte Informatik
Fakultät für Angewandte Informatik / Institut für Informatik
Dewey Decimal Classification:0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme / 004 Datenverarbeitung; Informatik